consult_specialist_modelAsk a specialist Gateway model for code, database/tool-calling, statistics, literature, preprint, or mechanistic synthesis support. Use for hard subproblems, second opinions, code generation/review, or complex synthesis.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Focused task for the specialist model. | |
| context | No | Relevant context, snippets, or data. Do not include secrets. | |
| task_type | Yes | Specialist routing target. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. While it says 'Ask a specialist Gateway model', it does not disclose behavioral traits such as latency, output format, token limits, cost, non-determinism, or side effects. The capability list (code, statistics, etc.) describes what it can be asked for, not how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with a front-loaded verb ('Ask') and immediately conveys purpose and usage. Every word earns its place; no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description lacks details about return value, limitations, or cost/performance considerations. It adequately introduces purpose and use cases, but leaves open questions about what the user will receive and any trade-offs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema includes descriptions for all parameters, so baseline is 3. The description adds a list of supported domains that partially mirrors the task_type enum, but does not deepen parameter understanding beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool asks a specialist Gateway model for support across code, database/tool-calling, statistics, literature, preprint, and synthesis domains. It distinguishes from sibling tools by positioning it as a consultation for hard subproblems, second opinions, and complex synthesis, not a direct search or computation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists usage scenarios: hard subproblems, second opinions, code generation/review, and complex synthesis. It provides clear context for when to use, but does not explicitly name alternatives or when not to use it, so it stops short of a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping responsibilities: search, search_claims, search_preprint_flags, and claidex_claim_risk_matrix all query claim/failure data, while rank_documents_by_embedding and rerank_documents both perform relevance ranking. The compatibility-oriented fetch/search tools add further confusion because their names collide with fetch_research_url and search_claims.
Names are grouped by prefixes (claidex_, query_, search_, run_) but the groups use different conventions, and bare verbs like 'fetch' and 'search' sit alongside prefixed forms like 'fetch_research_url' and 'search_claims'. The pattern is readable but not uniform.
24 tools is at the heavy end for an MCP server; while the breadth reflects many biomedical data sources and utilities, the count includes several meta/compatibility tools that could be consolidated. It is borderline but not unreasonable.
The surface covers the core biomedical workflows: searching claims, retrieving full claim content, querying failure graphs, checking preprints, and looking up drugs/trials/targets/adverse events. Minor gaps exist, such as no direct way to fetch a single clinical trial by ID beyond the search function, and no write/update operations for claims, but these are likely outside the read-only research scope.